Augmented reality navigation for cranial biopsy and external ventricular drain insertion.

OBJECTIVE: The aim of this study was to evaluate the accuracy (deviation from the target or intended path) and efficacy (insertion time) of an augmented reality surgical navigation (ARSN) system for insertion of biopsy needles and external ventricular drains (EVDs), two common neurosurgical procedures that require high precision. METHODS: The hybrid operating room-based ARSN system, comprising a robotic C-arm with intraoperative cone-beam CT (CBCT) and integrated video tracking of the patient and instruments using nonobtrusive adhesive optical markers, was used. A 3D-printed skull phantom with a realistic gelatinous brain model containing air-filled ventricles and 2-mm spherical biopsy targets was obtained. After initial CBCT acquisition for target registration and planning, ARSN was used for 30 cranial biopsies and 10 EVD insertions. Needle positions were verified by CBCT. RESULTS: The mean accuracy of the biopsy needle insertions (n = 30) was 0.8 mm ± 0.43 mm. The median path length was 39 mm (range 16-104 mm) and did not correlate to accuracy (p = 0.15). The median device insertion time was 149 seconds (range 87-233 seconds). The mean accuracy for the EVD insertions (n = 10) was 2.9 mm ± 0.8 mm at the tip with a 0.7° ± 0.5° angular deviation compared with the planned path, and the median insertion time was 188 seconds (range 135-400 seconds). CONCLUSIONS: This study demonstrated that ARSN can be used for navigation of percutaneous cranial biopsies and EVDs with high accuracy and efficacy.

Multi-view 3D skin feature recognition and localization for patient tracking in spinal surgery applications.

BACKGROUND: Minimally invasive spine surgery is dependent on accurate navigation. Computer-assisted navigation is increasingly used in minimally invasive surgery (MIS), but current solutions require the use of reference markers in the surgical field for both patient and instruments tracking. PURPOSE: To improve reliability and facilitate clinical workflow, this study proposes a new marker-free tracking framework based on skin feature recognition. METHODS: Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Feature (SURF) algorithms are applied for skin feature detection. The proposed tracking framework is based on a multi-camera setup for obtaining multi-view acquisitions of the surgical area. Features can then be accurately detected using MSER and SURF and afterward localized by triangulation. The triangulation error is used for assessing the localization quality in 3D. RESULTS: The framework was tested on a cadaver dataset and in eight clinical cases. The detected features for the entire patient datasets were found to have an overall triangulation error of 0.207 mm for MSER and 0.204 mm for SURF. The localization accuracy was compared to a system with conventional markers, serving as a ground truth. An average accuracy of 0.627 and 0.622 mm was achieved for MSER and SURF, respectively. CONCLUSIONS: This study demonstrates that skin feature localization for patient tracking in a surgical setting is feasible. The technology shows promising results in terms of detected features and localization accuracy. In the future, the framework may be further improved by exploiting extended feature processing using modern optical imaging techniques for clinical applications where patient tracking is crucial.

Frameless Patient Tracking With Adhesive Optical Skin Markers for Augmented Reality Surgical Navigation in Spine Surgery.

STUDY DESIGN: Observational study. OBJECTIVE: The aim of this study was to evaluate the accuracy of a new frameless reference marker system for patient tracking by analyzing the effect of vertebral position within the surgical field. SUMMARY OF BACKGROUND DATA: Most modern navigation systems for spine surgery rely on a dynamic reference frame attached to a vertebra for tracking the patient. This solution has the drawback of being bulky and obstructing the surgical field, while requiring that the dynamic reference frame is moved between vertebras to maintain accuracy. METHODS: An augmented reality surgical navigation (ARSN) system with intraoperative cone beam computed tomography (CBCT) capability was installed in a hybrid operating room. The ARSN system used input from four video cameras for tracking adhesive skin markers placed around the surgical field. The frameless reference marker system was evaluated first in four human cadavers, and then in 20 patients undergoing navigated spine surgery. In each CBCT, the impact of vertebral position in the surgical field on technical accuracy was analyzed. The technical accuracy of the inserted pedicle devices was determined by measuring the distance between the planned position and the placed pedicle device, at the bone entry point. RESULTS: The overall mean technical accuracy was 1.65 ±â€Š1.24 mm at the bone entry point (n = 366). There was no statistically significant difference in technical accuracy between levels within CBCTs (P ≥ 0.12 for all comparisons). Linear regressions showed that null- to negligible parts of the effect on technical accuracy could be explained by the number of absolute levels away from the index vertebrae (r ≤ 0.007 for all, ß ≤ 0.071 for all). CONCLUSION: The frameless reference marker system based on adhesive skin markers is unobtrusive and affords the ARSN system a high accuracy throughout the navigated surgical field, independent of vertebral position. LEVEL OF EVIDENCE: 3.

Towards Optical Imaging for Spine Tracking without Markers in Navigated Spine Surgery.

Surgical navigation systems are increasingly used for complex spine procedures to avoid neurovascular injuries and minimize the risk for reoperations. Accurate patient tracking is one of the prerequisites for optimal motion compensation and navigation. Most current optical tracking systems use dynamic reference frames (DRFs) attached to the spine, for patient movement tracking. However, the spine itself is subject to intrinsic movements which can impact the accuracy of the navigation system. In this study, we aimed to detect the actual patient spine features in different image views captured by optical cameras, in an augmented reality surgical navigation (ARSN) system. Using optical images from open spinal surgery cases, acquired by two gray-scale cameras, spinal landmarks were identified and matched in different camera views. A computer vision framework was created for preprocessing of the spine images, detecting and matching local invariant image regions. We compared four feature detection algorithms, Speeded Up Robust Feature (SURF), Maximal Stable Extremal Region (MSER), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated BRIEF (ORB) to elucidate the best approach. The framework was validated in 23 patients and the 3D triangulation error of the matched features was < 0 . 5 mm. Thus, the findings indicate that spine feature detection can be used for accurate tracking in navigated surgery.

Feasibility and accuracy of a robotic guidance system for navigated spine surgery in a hybrid operating room: a cadaver study.

The combination of navigation and robotics in spine surgery has the potential to accurately identify and maintain bone entry position and planned trajectory. The goal of this study was to examine the feasibility, accuracy and efficacy of a new robot-guided system for semi-automated, minimally invasive, pedicle screw placement. A custom robotic arm was integrated into a hybrid operating room (OR) equipped with an augmented reality surgical navigation system (ARSN). The robot was mounted on the OR-table and used to assist in placing Jamshidi needles in 113 pedicles in four cadavers. The ARSN system was used for planning screw paths and directing the robot. The robot arm autonomously aligned with the planned screw trajectory, and the surgeon inserted the Jamshidi needle into the pedicle. Accuracy measurements were performed on verification cone beam computed tomographies with the planned paths superimposed. To provide a clinical grading according to the Gertzbein scale, pedicle screw diameters were simulated on the placed Jamshidi needles. A technical accuracy at bone entry point of 0.48 ± 0.44 mm and 0.68 ± 0.58 mm was achieved in the axial and sagittal views, respectively. The corresponding angular errors were 0.94 ± 0.83° and 0.87 ± 0.82°. The accuracy was statistically superior (p < 0.001) to ARSN without robotic assistance. Simulated pedicle screw grading resulted in a clinical accuracy of 100%. This study demonstrates that the use of a semi-automated surgical robot for pedicle screw placement provides an accuracy well above what is clinically acceptable.

Design and control of an image-guided robot for spine surgery in a hybrid OR.

BACKGROUND: Minimally invasive spine (MIS) fusion surgery requires image guidance and expert manual dexterity for a successful, efficient, and accurate pedicle screw placement. Operating room (OR)-integrated robotic solution can provide precise assistance to potentially minimize complication rates and facilitate difficult MIS procedures. METHODS: A 5-degrees of freedom robot was designed specifically for a hybrid OR with integrated surgical navigation for guiding pedicle screw pilot holes. The system automatically aligns an instrument following the surgical plan using only instrument tracking feedback. Contrary to commercially available robotic systems, no tracking markers on the robotic arm are required. The system was evaluated in a cadaver study. RESULTS: The mean targeting error (N = 34) was 1.27±0.57 mm and 1.62±0.85°, with 100% of insertions graded as clinically acceptable. CONCLUSIONS: A fully integrated robotic guidance system, including intra-op imaging, planning, and physical guidance with optimized robot design and control, can improve workflow and provide pedicle screw guidance with less than 2 mm targeting error.

Towards non-invasive patient tracking: optical image analysis for spine tracking during spinal surgery procedures.

Surgical navigation systems can enhance surgeon vision and form a reliable image-guided tool for complex interventions as spinal surgery. The main prerequisite is successful patient tracking which implies optimal motion compensation. Nowadays, optical tracking systems can satisfy the need of detecting patient position during surgery, allowing navigation without the risk of damaging neurovascular structures. However, the spine is subject to vertebrae movements which can impact the accuracy of the system. The aim of this paper is to investigate the feasibility of a novel approach for offering a direct relationship to movements of the spinal vertebra during surgery. To this end, we detect and track patient spine features between different image views, captured by several optical cameras, for vertebrae rotation and displacement reconstruction. We analyze patient images acquired in a real surgical scenario by two gray-scale cameras, embedded in the flat-panel detector of the C-arm. Spine segmentation is performed and anatomical landmarks are designed and tracked between different views, while experimenting with several feature detection algorithms (e.g. SURF, MSER, etc.). The 3D positions for the matched features are reconstructed and the triangulation errors are computed for an accuracy assessment. The analysis of the triangulation accuracy reveals a mean error of 0.38 mm, which demonstrates the feasibility of spine tracking and strengthens the clinical application of optical imaging for spinal navigation.